There is a growing interest in the multiagent Reinforcement Learning (RL) community for spatial versions of Game Theory (GT) scenarios that can be seen as the first steps towards embodied ecologically valid setups in which agents learn to make decisions in complex environments, including complex reward contingencies [1]} and in the presence of other competitive learning agents [2]}, [3]} (to name a few). Recently economic notions, like loss aversion, have been incorporated to Reinforcement Learning settings [4]} something that we also do in this paper with the objective to fit behavioural human data of [5]} as we also started doing in [6]}.
